1. Field of the Invention
The present invention is in the field of brain-controlled devices and processes. More specifically, the present invention provides a brain computer interface as an alternative communication channel to be used in various applications, such as robotics. In one embodiment of the invention, there is provided a process for the analysis and conversion of EEG signals obtained from the brain into movement commands through electric and/or mechanical devices. The process of the present invention provides substantial advantages over the similar systems/techniques known in the art, such as a 91% average hit rate, obtained in attempts to control a mobile robot. In other embodiment of the invention, there is provided an apparatus comprising: means for obtaining brain signals; an electroencephalograph (EEG); and means for transducing said signals into functional commands useful in several applications. Said means for transducing mental signals is the core of the invention and provides a number of technical advantages over the similar systems/techniques known in the art of identifying mental activities.
2. Prior Art
The development of interfaces between humans and machines has been an expanding field in the last decades. It includes several interfaces using voice, vision, haptics, electromyography, electroencephalography (EEG), and combinations among them as a communication support.1 A system that analyzes brainwaves to derive information about the subjects' mental state is called a Brain Computer Interface (BCI).2 
People who are partially or totally paralyzed (e.g., by amyotrophic lateral sclerosis (ALS) or brainstem stroke) or have other severe motor disabilities can find a BCI as an alternative communication channel.3 BCI systems are used to operate a number of brain-actuated applications that augment people's communication capabilities, provide new forms of education and entertainment, and also enable the operation of physical devices.2 
There are two types of BCI's: invasive, which are based on signals recorded from electrodes implanted over the brain cortex (requiring surgery), and non-invasive, based on the analysis of EEG phenomena associated with various aspects of brain function.1 
Birbaumer measured slow cortical potentials (SCP) over the vertex (top of the scalp).4 SCP are shifts in the depolarization level of the upper cortical dendrites, which indicate the overall preparatory excitation level of a cortical network. Other groups looked at local variations of the EEG rhythms. The most used of such rhythms are related to the imagination of movements, recorded from the central region of the scalp overlying the sensorimotor and pre-sensorimotor cortex. In this respect, there are two main paradigms. Pfurtscheller's team worked with event-related desynchronization (ERD) computed at fixed time intervals after the subject is commanded to imagine specific movements of the limbs.5, 6 Alternatively, Wolpaw and coworkers analyzed continuous changes in the amplitudes of the mu (8-12 Hz) or beta (13-28 Hz) rhythms.7 
Finally, in addition to motor-related rhythms, Anderson and Millán analyzed continuous variations of EEG rhythms, but not only over the sensorimotor cortex and in specific frequency bands.8, 9 The reason is that a number of neurocognitive studies have found that different mental activities (such as imagination of movements, arithmetic operations, or language) activate local cortical areas at different extents. The insights gathered from these studies guide the placement of electrodes to get more relevant signals for the different tasks to be recognized.
BCI applications include control of the elements in a computer-rendered environment such as cursor positioning1, 3 or visiting of a virtual apartment.10 spelling software,11 and command of an external device such as a robot12 or prosthesis.13 Recent applications in Robotics are the control of a wheelchair14-16 and the control of the Khepera mobile robot.17 
The scientific literature which appears in the present invention is the following:
1. G. N. Garcia, “Direct brain-computer communication through scalp recorded EEG signals,” Doctor's thesis, Department of Electricity, Ecole Polytechnique Fédérale de Lausanne, Switzerland, 2004.
2. J. d. R. Millán, Brain-Computer Interfaces, Handbook of Brain Theory and Neural Networks, 2nd ed., Cambridge, Mass., The MIT Press, 2002.
3. J. R. Wolpaw, D. J. McFarland, T. M. Vaughan, “Brain Computer Interface Research at the Wadsworth Center,” IEEE Transactions on Neural Systems and Rehab. Eng., vol. 8, pp.222-226, 2000.
4. N. Birbaumer, “A spelling device for the paralyzed,” Nature, vol. 398, pp. 297-298, 1999.
5. J. Kalcher, “Graz brain-computer interface II,” Med. & Biol. Eng. & Comput., vol. 34, pp. 382-388. 1996.
6. B. Obermaier, C. Neuper, C. Guger, G. Pfurtscheller, “Information Transfer Rate in a Five-Classes Brain Computer Interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 9, no. 3, pp. 283-288, 2001.
7. J. R. Wolpaw, D. J. McFarland, “Multichannel EEG-based brain-computer communication,” Electroenceph. Clin. Neurophysiol., vol. 90, pp. 444-449, 1994.
8. C. W. Anderson, “Effects of variations in neural network topology and output averaging on the discrimination of mental tasks from spontaneous EEG,” Journal of Intelligent Systems, vol. 7, pp. 165-190, 1997.
9. J. d. R. Millán, Brain-Computer Interfaces, Handbook of Brain Theory and Neural Networks, Second edition, Cambridge, Mass., The MIT Press, 2002.
10. J. D. Bayliss, “Use of the Evoked Potential P3 Component for Control in a Virtual Apartment,” IEEE Transactions Rehabilitation Engineering, vol. 11, no. 2, pp. 113-116, 2003.
11. B. Obermaier, G. Müller, G. Pfurtscheller, “‘Virtual Keyboard’ controlled by spontaneous EEG activity,” Proc. of the Int. Conference on Artificial Neural Networks, Heidelberg: Springer-Verlag, 2001.
12. J. d. R. Millán and J. Mourino, “Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 159-161, 2003.
13. G. Pfurtscheller, C. Neuper, G. R. Muller, B. Obermaier, G. Krausz, A. Schlogl, R. Scherer, B. Graimann, C. Keinrath, D. Skliris, M. Wortz, G. Supp, C. Schrank, “Graz-bci: State of the Art and Clinical Applications,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 177-180, 2003.
14. B. Rebsamen, E. Burdet, C. Guan, C. L. Teo, Q. Zeng, M. Ang, C. Laugier, “Controlling a wheelchair using a BCI with low information transfer rate,” IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1003-1008, 2007.
15. F. Galán, M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips and J. d. R. Millán, “A Brain-Actuated Wheelchair: Asynchronous and Non-Invasive Brain-Computer Interfaces for Continuous Control of Robots,” Clinical Neurophysiology, vol. 119, pp. 2159-2169, 2008.
16. I. Iturrate, J. Antelis, A. Kübler, J. Minguez, “A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation,” IEEE Transactions on Robotics, vol. 99, pp. 1-14, 2009.
17. J. d. R. Millán, F. Renkens, J. Mouriño, W. Gerstner, “Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp 1026-1033, 2004.
18. A. R. Cotrina, “Sistemas de adquisición y procesamiento de las señales del cerebro,” B.Sc. Thesis, Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Lima, Peru, 2003.
19. M. Benning, S. Boyd, A. Cochrane, D. Uddenberg, “The Experimental Portable EEG/EMG Amplifier,” ELEC 499A Report, University of Victoria, Faculty of Engineering, August 2003.
20. National Instruments, “Build Your Own NI CompactDAQ System.” [Online]. Available: http://ohm.ni.com/advisors/compactdaq
21. C. Tandonnet, B. Burle, T. Hasbroucq, F. Vidal, “Spatial enhancement of EEG traces by surface Laplacian estimation: comparison between local and global methods,” Clinical Neurophysiology, vol. 116, no. 1, pp. 18-24, 2005.
22. P. Jahankhani, V. Kodogiannis, K. Revett, “EEG signal classification using wavelet feature extraction and neural networks,” IEEE John Vincent Atanasoff International Symposium on Modern Computing, pp. 120-124, 2006.
23. M. Van de Velde, G. Van Erp, P. J. M. Cluitmans, “Detection of muscle artifact in the normal human awake EEG,” Electroencephalography and Clinical Neurophysiology, vol. 107, no. 2, pp. 149-158, 1998.
24. D. R. Achanccaray, M. A. Meggiolaro, “Brain Computer Interface Based on Electroencephalographic Signal Processing,” XVI IEEE International Congress of Electrical, Electronic and Systems Engineering—INTERCON 2009, Arequipa, Peru, 2009.
25. A. O. G. Barbosa, D. R. Achanccaray, M. Vellasco, M. A. Meggiolaro, R. Tanscheit, “Mental Tasks Classification for a Noninvasive BCI Application”, 19th International Conference on Artificial Neural Networks, ICANN'09, Limassol, Cyprus, 2009.
26. F. Findji, P. Catani, C. Liard, “Topographical distribution of delta rhythms during sleep: Evolution with age,” Electroencephalography and Clinical Neurophysiology, vol. 51, no. 6, pp. 659-665, 1981.
27. Endurance R/C, “PCTx—PC to Transmitter Interface.” [Online]. Available: http://www.endurance-rc.com/pctx.html.
To the best knowledge of the inventors, neither any document anticipating the present invention was found nor the combination of the existing ones even suggest the subject-matter of the invention.